A novel method for artery detection in laparoscopic surgery

Feeling of pulsations in artificial arteries with a real time haptic feedback laparoscopic grasper: a validation study

INTRODUCTION

Despite the advancements in technology and organized training for surgeons in laparoscopic surgery, the persistent challenge of not being able to feel the resistance and characteristics of the tissue, including pulsations, remains unmet. A recently developed grasper (Optigrip®) with real time haptic feedback, based on photonic technology, aims to address this issue by restoring the tactile sensation for surgeons. The key question is whether pulsations can be detected and at what minimal size level they become clinical significant.

METHODS

To simulate arterial conditions during laparoscopic procedures, four different silicone tubes were created, representing the most prevalent arteries. These tubes were connected to a validated pressure system, generating a natural pulse ranging between 80 and 120 mm Hg. One control tube without pressure was added. The surgeons had to grasp these tubes blindly with the conventional grasper or the haptic feedback grasper in a randomized order. They then indicated whether they felt the pressure or not and the percentage of correct answers was calculated.

RESULTS

The haptic grasper successfully detected 96% of all pulsations, while the conventional grasper could only detect 6%. When considering the size of the arteries, the Optigrip® identified pulsations in 100% the 4 and 5 mm arteries and 92% of the smallest arteries. The conventional grasper was only able to feel the smallest arteries in 8%. These differences were highly significant (p < 0.0001).

CONCLUSION

This study demonstrated that the newly developed haptic feedback grasper enables detection of arterial pulsations during laparoscopy, filling an important absence in tactile perception within laparoscopic surgery.

HyperVein: A Hyperspectral Image Dataset for Human Vein Detection

HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward's Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.

Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review

BACKGROUND

Minimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and improve learning curves. The study objective was to provide a comprehensive overview of current literature on the accuracy of anatomy recognition algorithms in intrathoracic and -abdominal surgery.

METHODS

This systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Pubmed, Embase, and IEEE Xplore were searched for original studies up until January 2022 on computer-aided anatomy recognition, without requiring intraoperative imaging or calibration equipment. Extracted features included surgical procedure, study population and design, algorithm type, pre-training methods, pre- and post-processing methods, data augmentation, anatomy annotation, training data, testing data, model validation strategy, goal of the algorithm, target anatomical structure, accuracy, and inference time.

RESULTS

After full-text screening, 23 out of 7124 articles were included. Included studies showed a wide diversity, with six possible recognition tasks in 15 different surgical procedures, and 14 different accuracy measures used. Risk of bias in the included studies was high, especially regarding patient selection and annotation of the reference standard. Dice and intersection over union (IoU) scores of the algorithms ranged from 0.50 to 0.98 and from 74 to 98%, respectively, for various anatomy recognition tasks. High-accuracy algorithms were typically trained using larger datasets annotated by expert surgeons and focused on less-complex anatomy. Some of the high-accuracy algorithms were developed using pre-training and data augmentation.

CONCLUSIONS

The accuracy of included anatomy recognition algorithms varied substantially, ranging from moderate to good. Solid comparison between algorithms was complicated by the wide variety of applied methodology, target anatomical structures, and reported accuracy measures. Computer-aided intraoperative anatomy recognition is an upcoming research discipline, but still at its infancy. Larger datasets and methodological guidelines are required to improve accuracy and clinical applicability in future research.

TRIAL REGISTRATION

PROSPERO registration number: CRD42021264226.

Surgical spectral imaging

Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and artificial intelligence (AI), especially in machine learning and deep learning, have made these data-rich modalities highly attractive as a means of extracting biological information non-destructively. Surgery in particular is poised to benefit from this, as spectrally-resolved tissue optical properties can offer enhanced contrast as well as diagnostic and guidance information during interventions. This is particularly relevant for procedures where inherent contrast is low under standard white light visualisation. This review summarises recent work in surgical spectral imaging (SSI) techniques, taken from Pubmed, Google Scholar and arXiv searches spanning the period 2013-2019. New hardware, optimised for use in both open and minimally-invasive surgery (MIS), is described, and recent commercial activity is summarised. Computational approaches to extract spectral information from conventional colour images are reviewed, as tip-mounted cameras become more commonplace in MIS. Model-based and machine learning methods of data analysis are discussed in addition to simulation, phantom and clinical validation experiments. A wide variety of surgical pilot studies are reported but it is apparent that further work is needed to quantify the clinical value of MSI/HSI. The current trend toward data-driven analysis emphasises the importance of widely-available, standardised spectral imaging datasets, which will aid understanding of variability across organs and patients, and drive clinical translation.

Reaction Force Mapping by 3-Axis Tactile Sensing With Arbitrary Angles for Tissue Hard-Inclusion Localization

Although robot-assisted diagnosis and minimally invasive surgery (MIS) brings distinct benefits, deficient multi-dimensional force feedback remains a noteworthy limitation and challenge in MIS. Aiming for a comprehensive high-fidelity perception of tissue-instrument interactions, we present a Fiber Bragg Grating (FBG)-based 3-axis tactile sensing for surface reaction force mapping, identification and localization of tissue hard-inclusion. The tactile sensing probe consists of five optical fibers inscribed with FBGs and a force-sensitive 3D printed deformable body. All fibers are suspended inside the deformable body in a parallel manner, leading to the direct compression or tension of each FBG. Such configuration can effectively avoid the chirping failure of FBG compared with the pasting FBG-based sensors. A linearized difference model is proposed to calibrate the 3-axis force detection and enhance the resistance to nonlinear interferences. Hard-inclusion identification experiments with varied hard-inclusion sizes and depths have been implemented through discrete palpation and dragging palpation modes. Results indicate that the probe can effectively identify the presence and location of these small hard-inclusions from the force mapping. Furthermore, lengthy vessels embedded in the phantom can be accurately identified through dragging palpation with an arbitrary contact angle. Another novelty of the probe is the reconstruction of the surface profile of a non-planar tissue, which further allows hard-inclusion identification and 3D localization. Ex-vivo tissue palpation on a porcine kidney further validates the effectiveness and feasibility of the probe to map surface reaction forces and localize the hard-inclusions intraoperatively.

A Face-Shear Mode Piezoelectric Array Sensor for Elasticity and Force Measurement

We present the development of a 6 × 6 piezoelectric array sensor for measuring elasticity and force. The proposed sensor employs an impedance measurement technique, sensing the acoustic load impedance of a target by measuring the electrical impedance shift of face-shear mode PMN-PT (lead magnesium niobate-lead titanate) single crystal elements. Among various modes of PMN-PT single crystals, the face-shear mode was selected due to its especially high sensitivity to acoustic loads. To verify the elasticity sensing performance, gelatin samples with different elastic moduli were prepared and tested. For the force measurement test, different magnitudes of force were loaded to the sensing layer whose acoustic impedance was varied with applied forces. From the experimental results, the fabricated sensor showed an elastic stiffness sensitivity of 23.52 Ohm/MPa with a resolution of 4.25 kPa and contact force sensitivity of 19.27 Ohm/N with a resolution of 5.19 mN. In addition, the mapping experiment of elasticity and force using the sensor array was successfully demonstrated.

Blood vessel detection and artery-vein differentiation using hyperspectral imaging

Blood vessel detection is an important but difficult task during surgeries. An unexpected location of a blood vessel or anatomical variations may result in an accidental injury to the blood vessel. This problem would extend the operation time or cause a serious complication. Moreover, differentiating the arteries from veins is necessary in majority of medical procedures. Hyperspectral imaging has entered as a new modality in medicine. This imaging and spectroscopic tool can be used for different applications including medical diagnosis. The unpredictable anatomy of blood vasculature during surgeries especially in anatomical variations makes the visibility very important. In this paper, a hyperspectral imaging technique is proposed as a visual supporting tool to detect blood vessels and to differentiate between the artery and vein during surgeries. This technique can aid the surgeon to find blood vessels and to diagnose normal anatomical variation and abnormalities. The hyperspectral images are captured using two cameras: a visible plus near infrared camera (400-1000nm) and an infrared camera (900-1700nm). Using hyperspectral images, a library of spectral signatures for abdominal organs, arteries, and veins are created. The high-dimensional data are classified using support vector machine (SVM). This method is evaluated for the detection of arteries and veins in abdominal surgeries on a pig.

Image-guided preparation of the Calot's triangle in laparoscopic cholecystectomy

Laparoscopic cholecystectomy is the most common way to remove the gallbladder nowadays. Compared to open surgery, laparoscopy results in shorter hospital stays, reduced postoperative pain, and smaller incisions. Proper localization of the cystic artery is of great importance in laparoscopic cholecystectomy in order to ensure safe stapling and avoiding injury to the artery. In this study, we evaluate an image-guided method for artery detection. The performance of this method was evaluated in detecting arteries in 35 laparoscopic cholecystectomy patients. This method uses the artery's pulse to distinguish it from veins and biliary ducts. By subtracting the systolic and diastolic images, the change regions are detected and shown on a monitor. In 35 laparoscopic cholecystectomy procedures the method can correctly detect all arteries that are not too deep and can move superficial tissues with zero false-negative and 12% false-positive rates. Using the second mode of the method that needs more time for processing, the false-positive rate decreased to 4% with zero false-negative. The image-guided technique is a sensitive, noninvasive, and cost-effective method to detect arteries in laparoscopic cholecystectomy, even if it is covered with fat or other tissues. It is possible to install the program on any ordinary laparoscopy set and it displays the artery's region on the monitor.

Gasless single-port access endoscopic surgery in urology: minimum incision endoscopic surgery, MIES

Abstract Minimum incision endoscopic surgery (MIES) is a gasless, single-port access, cost-effective, and minimally invasive surgery that has been in development since the late 1990s. Use of MIES has steadily increased in Japan and Asia and has been introduced into Europe and the USA. In 2006, MIES was certified by the Japanese government as an advanced surgery and since 2008 it has been covered by the Japanese universal health insurance system as a new surgical technique. Briefly, MIES involves an initial minimum incision (a single port) that permits extraction of the target specimen. A wide working space through the port is then made by separating the anatomical plane extraperitoneally. This is maintained with special retractors instead of gas insufflation. All instruments including an endoscope are inserted through the port and the operation is completed. The size of the port can be tailored to the situation if necessary, which contributes to preclusion of patient selection. The procedure uses only two disposable devices that are inexpensive, resulting in low equipment costs. Surgeons have the benefits of magnified vision through endoscopy as well as stereovision and panoramic vision of naked eyes through the port, which reduces the technical demands of the procedure. Techniques for two basic MIES procedures allow MIES to be performed for most urological organs and in extraordinary cases by their modifications. Thus, the MIES system permits minimally invasive surgery without use of CO(2) gas, which is ideal from medical, environmental and economic perspectives, is cost-effective and minimizes patient selection.